New AI tool cuts breast cancer biopsy wait times

Women with abnormal mammograms often have to wait for weeks to find out whether they have breast cancer.

Now, researchers at UC San Francisco and UC Berkeley have found a way to help reduce the wait and the worry by using AI to quickly identify those who are most likely to have the disease. By triaging these patients, the AI-guided workflow takes women with abnormal scans through the diagnostic process - from imaging to evaluation and sometimes even biopsy - in a single day. 

"This is a really an exciting time," said Maggie Chung, MD, the first author of the study, which was published May 19 in Nature Digital Medicine. "This moves us closer to personalized care, where we can tailor a plan so that each patient gets the right intervention at the right time."

Researchers used an open-source AI model called Mirai, which was developed by the study's senior author, UC Berkeley data scientist Adam Yala, PhD. After being trained on hundreds of thousands of mammograms that were linked to patients' cancer outcomes, the model can recognize subtle patterns in a screening mammogram and predict a woman's cancer risk in a more powerful way than a physician working alone. 

Chung and Yala applied the model to more than 4,100 screening mammograms at Zuckerberg San Francisco General Hospital and Trauma Center. Mirai determined that 525 women - about 12.7% of screened patients - were high risk.

Those patients could get an interpretation of their mammograms right after having them done and have additional diagnostic imaging for any suspicious areas the same day. Some of the women who needed biopsies were able to have that done the same day as well.

Mirai reduced the wait time for a diagnostic evaluation from several weeks to about an hour. And for those who were ultimately diagnosed with breast cancer, Mirai reduced the average wait for a biopsy from more than two months to fewer than 10 days.

Mirai does not replace radiologists - or make diagnoses on its own. Instead, it's a triage tool that helps physicians identify the patients who can benefit most from accelerated care.

"This is a powerful example of how AI can be a collaborative partner for physicians," said Yala, who along with Chung is an assistant professor in the UCSF-UC Berkeley Joint Program in Computational Precision Health. "It shows how we can improve care when we bring clinicians and data scientists together to design these systems."

The researchers analyzed more than 114,000 archival mammograms before launching the program, to ensure the model would capture enough high-risk patients without overloading the clinic with too many expedited evaluations.

The researchers hope that AI will foster a more personalized approach to breast cancer screening that is tailored to each patients' breast cancer risk. 

Right now, many women follow the same screening schedule but their individual risk can be very different. AI risk assessment gives us the chance to identify the women most likely to benefit from expedited care and get them what they need."

Maggie Chung, MD, first author of the study